Methods and apparatus for sea state measurement via radar sea clutter eccentricity

ABSTRACT

Methods and apparatus to fit the range extent of radar sea clutter to an ellipse to determine sea state. From one or more ellipse parameters, a sea state, which can include direction, can be identified. In one embodiment, the system autonomously determines the sea state and automatically selects non-isotropic STC filtering based on the ellipse that measures the sea state.

BACKGROUND

As is known in the art, the roughness of the ocean surface is describedby the convention of sea state and direction. There are several knownsea state conventions, such as the World Meteorological Organization seastate code and the Douglas scale. United States Navy traditions fordescribing sea state have come from the “American Practical Navigator,”by N. Bowditch, while Europeans often specify sea state indirectlythrough reference to the Beaufort wind scale.

Related to sea state, but with more precision, is the notion of waveheight described as “significant wave height” or Root Mean Squared (RMS)wave height. These descriptive single values summarize the scientificdescription of the ocean surface that requires a variety of parameters.In particular, the sea surface is more precisely described usingspectral parameters of the waves.

As is known in the art, sea state estimation can be an importantconsideration in many applications, such as oil platform operation, safeboat handling, navigation, and sensor (radar) performance prediction.Traditionally, experienced mariners have visually estimated sea state,but this technique is somewhat arbitrary.

SUMMARY

The present invention provides methods and apparatus for sea statemeasurement from one or more parameters of an ellipse fitted to radarsea clutter. With this arrangement, sea state can be autonomouslydetermined. In one embodiment, Sensitivity Time Control (STC) filteringcan be automatically selected based upon sea state and/or sea clutterinformation. While exemplary embodiments of the invention are shown anddescribed in conjunction with particular data, ellipse processingtechniques, and radars, it is understood that embodiments of theinvention are applicable to radars in general for which it is desirableto determine sea state.

In one aspect of the invention, a method comprises receiving radarreturn information from signals transmitted by a radar, processing theradar return information to identify sea clutter, and processing, usinga processor, the sea clutter to fit an ellipse to a range horizon of thesea clutter as a function of azimuth to determine a sea state.

The method can further include one or more of the following features:determining a direction for the sea state based upon at least oneparameter of the ellipse, generating an alert when the range horizon ofthe sea clutter does not fit an ellipse with a predetermined criteria,generating an alert when the range horizon for the sea clutter cannot bedetermined, using ratios of order statistics to form a range profile forthe sea clutter, determining the range horizon for the sea clutter froma corner point of the range profile from the ratios of order statistics,determining the direction for the sea state from a direction of themajor axis of the ellipse, determining the sea state from aneccentricity of the ellipse, determining the sea state from a ratio of alength of an offset of the ellipse to a semi-major axis of the ellipse,and/or mapping at least one parameter of the ellipse to sea states,using a Fourier Transform to determine one or more parameters of theellipse.

In another aspect of the invention, an article comprises non-transitoryinstructions stored on a computer readable medium to enable a machine toperform: receiving radar return information from signals transmitted bya radar, processing the radar return information to identify seaclutter, and processing the sea clutter to fit an ellipse to a rangehorizon of the sea clutter to determine a sea state.

The article can further include one or more of the following features:determining a direction for the sea state based upon at least oneparameter of the ellipse, generating an alert when the range horizon ofthe sea clutter does not fit an ellipse with a predetermined criteria,generating an alert when the range horizon for the sea clutter cannot bedetermined, using ratios of order statistics to form a range profile forthe sea clutter, determining the range horizon for the sea clutter froma corner point of the range profile from the ratios of order statistics,determining the direction for the sea state from a direction of themajor axis of the ellipse, determining the sea state from aneccentricity of the ellipse, determining the sea state from a ratio of alength of an offset of the ellipse to a semi-major axis of the ellipse,and/or mapping at least one parameter of the ellipse to sea states,using a Fourier Transform to determine one or more parameters of theellipse.

In a further aspect of the invention, a radar system comprises: anantenna to receive radar return, and a processor to process the radarreturn to identify sea clutter and fit a range horizon of the seaclutter to an ellipse to determine a sea state. The system can determinea direction for the sea state based upon at least one parameter of theellipse.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing features of this invention, as well as the inventionitself, may be more fully understood from the following description ofthe drawings in which:

FIG. 1 is a pictorial representation of a shipboard radar system havingsea state processing in accordance with exemplary embodiments of theinvention;

FIG. 1A is a schematic representation of azimuth for the radar system ofFIG. 1;

FIG. 2 is a pictorial representation of a plan position indicator (PPI)showing sea clutter return with no STC applied;

FIG. 3 is a graphical representation of recorded sea clutter amplitudereturn versus range;

FIG. 4 is a graphical representation of flattened sea clutter overrange;

FIG. 5 is a schematic representation of an exemplary radar systemproviding sea state processing in accordance with exemplary embodimentsof the invention;

FIG. 6 is a pictorial representation of a radar return display showingelliptical sea clutter;

FIG. 7 is a flow diagram showing an exemplary sequence of steps forimplementing sea state processing;

FIG. 8 is a graphical representation of range bin versus clutter;

FIG. 9 is a graphical representation of model fit to mean sea clutter;

FIG. 10 is a graphical representation of range bin versus land clutter;

FIG. 11 is a graphical representation of raw radar video;

FIG. 12 is a graphical representation of the radar video of FIG. 11 in adifferent format;

FIG. 13 is a graphical representation of clutter averages versus OrderStatistic Ratios;

FIGS. 14A and 14B are graphical representations of Z-curves fit torespective Order Statistic Ratios;

FIG. 15 is a graphical representation of radial amplitude for a centeredellipse;

FIG. 16 is a pictorial representation of a PPI with a blank sector ofsea clutter;

FIG. 17 is a block diagram of an exemplary computer to performprocessing.

DETAILED DESCRIPTION

FIG. 1 shows an exemplary radar system 100 having sea state processingin accordance with exemplary embodiments of the invention. The radarsystem 100 can be located on a vehicle 10, such as a ship, or at a fixedlocation. The radar system 100 includes a signal processing module 102and a sea state processing module 104 to automatically identify seastate, as discussed in detail below.

In general, exemplary embodiments of the present invention include seastate estimation based upon eccentricity of detected sea clutter and/orother parameters of an ellipse fitted to the sea clutter. Mean seaclutter levels are a function of range, referred to as the rangeprofile, as well as azimuth. That is, sea clutter is elliptical, i.e.,non-isotropic, as described, for example, in FIG. 9.23 on page 295 of“Sea Clutter: Scattering, the K Distribution and Radar Performance,” byKeith D. Ward, Robert J. A. Tough and Simon Watts, IET Publishers,London, 2006. It is understood that conventional sea clutter models areisotropic, i.e., do not take azimuth into account when processing forSTC and/or extraction of targets using a technique of Constant FalseAlarm Rate (CFAR) thresholding. As is known in the art, azimuth refersto an angle in a spherical coordinate system defined as the angle αbetween a reference point RP and a point of interest POI for an observerO, as shown in FIG. 1A.

Prior to describing exemplary embodiments of the invention in detail,some information is provided. The purpose of STC is to aid in theelimination of average radar signal return from the sea surface near theradar antenna. Sea clutter is modeled to enhance filter design, e.g.,sensitivity time control (STC), to reduce the sea clutter withoutadversely affecting radar performance. STC attenuates relatively strongsignal returns from ground clutter targets in the first few range gatesof the receiver to avoid saturation from strong signal return.

Known sea clutter models for STC are isotropic, i.e., have the samemagnitude measured in all directions. An isotropic radiator radiatesuniformly in all directions over a sphere centered on the source.However, as shown in FIG. 2, for example, sea clutter 10 is notisotropic. That is, as can be been the sea clutter is elongate, ratherthan circular. Strong winds can generate a significantly elongated seaclutter region from the perspective of the radar of interest. Theillustrated return is from a navigation radar with STC turned offresulting in saturation about the radar.

In accordance with the standard Radar Equation, the average strength ofsea clutter echo diminishes rapidly with increasing range from theantenna. One of the difficulties in implementing sea clutter reductionis in responding to the rapid rate of decrease in its average strength.

FIG. 3 shows actual digital radar video demonstrating the rapid decreasein sea clutter signals SC for various pulse repetition interval (PRI)signals as they ultimately diminish to the low level of receiver noise.Also shown is an a priori smooth curve MSC to match the mean (average)shape of the actual sea clutter. As noted above, mathematicaldescriptions of such sea clutter models are well known. Subtracting thecurve chosen to model the mean sea clutter (as a function of range), theSTC function “flattens” the receiver output RO, as shown in FIG. 4.

In legacy analog systems, STC is performed in the receiver, as close tothe radar antenna as possible since detection of targets at close range,as well as far range, drives the need for wide dynamic range in eachsystem component downstream from the STC function.

In particular, older navigation radars typically terminate their analogvideo processing with a log-amp detector, which has a restricted dynamicrange that hinders short-range detection. Modern designers have made thetrade-off to spend more on wider dynamic range in the log-amp ratherthan implement STC in the Intermediate Frequency (IF) portion of thesuperheterodyne receiver. Part of the reason for this “sub-optimal”implementation of STC is the difficulty in implementing arbitrarilyshaped mean sea clutter curves in analog circuits controllable forchanging weather conditions.

More recent systems implement STC in analog circuits on circuit cardassemblies after the log-amplifier. In these implementations, a digitalcurve is generated to model mean sea clutter based on sea stateinformation from a human operator. That is, the human operator selects asea state on perceived or measured information. The curve is transferredto a Digital to Analog Converter (DAC) whose output voltage controls avariable attenuator acting on the input analog video. This attenuationapproach to STC is generally referred to as “Multiplicative STC.”So-called “Subtractive STC” is shown in FIG. 4.

One flaw in traditional STC processing is that the curve selected by anoperator input is fixed for all azimuth angles of antenna orientationrelative to the sea, i.e., the sea clutter model is isotropic. However,it is well-known that radar sea clutter strength varies significantlywith changing aspect angles from the antenna into the waves of highersea states, e.g., larger waves. FIG. 2 depicts a rather calm sea state.Higher sea states can further elongate the sea clutter.

FIG. 5 shows an exemplary radar system 200 to provide sea clutterprocessing and automatic sea state determination in accordance withexemplary embodiments of the invention. The system 200 includes a RFchannel select and RF amplifier 202 coupled to a down-converter andbandwidth filter 204 proving input to a Log-Amp detector 206.

A STC attenuator 208 includes a sea clutter module 209 to provide seaclutter processing in accordance with exemplary embodiments of theinvention. In one embodiment, the maximum detectable range sea clutteras it varies with azimuth is fitted to an ellipse. As described morefully below, a sea state module 211 determines sea state and directionfrom parameters of the ellipse and enables automatic (non-isotropic) STCin log-FTC receivers. Based on the detected sea state and sea clutterinformation, the system automatically selects STC filtering for seaclutter.

A range profile generation module 210 provides data to aDigital-to-Analog Converter module 212, which provides information tothe STC attenuator 208.

A gain amplifier 214 receives output from the STC attenuator 208 andprovides data for digitization by an Analog-to-Digital converter module216. This data then passes through an interference rejection filtermodule 214, a non-coherent integration module 216, and an FTC module218. An operator can optionally interact with the system via a userinterface 221. For example, a user can adjust bias settings to optimizewhat is shown on a display 223.

As described in U.S. Pat. No. 7,796,082 to Wood, which is incorporatedby reference, a cubic polynomial can used for the STC curve. The curvecan be expressed as follows:

${STC}_{i} = \left\{ \begin{matrix}{{\mu + {\frac{\xi - \mu}{h^{3}}\left( {h - i} \right)^{3}}},} & {{{if}\mspace{14mu} i} \leq h} \\\mu & {{{if}\mspace{14mu} i} > h}\end{matrix} \right.$

where i is the range index (variable), h is the maximum range at whichsea clutter is presently detectable, μ is the mean noise level in theradar, and ξ is the peak value of sea clutter at close range to theantenna. The values of μ and ξ depend primarily on the pulsewidth modesetting. The value of h (also called the clutter horizon) depends on thepulsewidth mode in use, the sea state, and the aspect angle of theantenna and the waves.

Thus, the STC curve in the '082 patent is sea state dependent. Knownsystems require an operator to select a sea state and manually input thesea state into the system.

In one aspect of the invention, an ellipse is used to describe the outerboundary (clutter horizon) of the sea clutter detected by non-coherentradar. As shown in FIG. 6, radar sea clutter, when not filtered, can beseen easily on the Plan Position Indicator (PPI) of a shipboardnavigation radar. The area near the center represents strong seaclutter, where the brighter the screen, the stronger the return. As canbe seen, the sea clutter horizon forms an ellipse. The illustrated seaclutter is for sea state 1 (4 nmi PPI).

In general, exemplary embodiments of the invention use an ellipse todescribe the clutter horizon as a function of azimuth and determine seastate. Using the parameters of the ellipse, the sea state and wavedirection can be automatically measured with access to the unprocesseddigital radar video for the radar system. The detected sea state can beused for automatic STC, as described more fully below.

FIG. 7 shows an exemplary processing diagram to determine sea state inaccordance with exemplary embodiments of the invention. In step 700, thedigital video for the radar is input to the system. In step 702, radarvideo data is collected for a sector S_(k) at azimuth α_(k). In step704, the sea clutter horizon h(α_(k)) is computed. In step 706, it isdetermined whether there is a good fit, as described more fully below.If not, in step 708 an alert is generated, and in step 710 the computedhorizon h(α_(k)) is discarded and processing continues for the nextsector in step 702. If there was a good fit, in step 712, it isdetermined whether data is collected for all sectors. If so, the data isfitted to an ellipse in step 714, as described more fully below. In step716, it is determined whether there is a good fit to the ellipse. Ifnot, in step 718 an alert is generated, the ellipse is discarded in step720, and processing continues in step 702. If there is a good fit to anellipse, in step 722 the sea state is determined. It is understood thatsea state can include wind direction. In step 724, the data isoptionally low pass filtered and in step 726 the data is output.

In step 728, the sea state is used to automatically select STC toattenuate radar return to remove sea clutter. As described in '082patent to Wood, since the parameters μ and ξ of the cubic polynomial arecalibrated based on radar system settings, it is the clutter horizon,h(α_(k)), that varies with sea state and with azimuth. By autonomouslyestimating h(α_(k)) and providing it to the range profile generationmodule 212 of FIG. 5 the STC Attenuator 208 is allowed to vary withazimuth for the resulting improved performance in detection of smalltargets in clutter.

In general, alerts may be generated when approaching a port, other shipsapproach, or other conditions that degrade clutter horizon detection andellipse fitting. For example, as a ship heads into port the sea clutterhorizon determination becomes problematic, as will be readilyappreciated by one of ordinary skill in the art. The alert can take theform of any type of signal that can be perceived by a radar operator orother personnel. Example alerts include visual indicia to a display,sounds, flashing, horns, vibrations, outgoing phone call, etc.

It is understood that processing in the various blocks can be performedin a variety of ways. For example, in one embodiment the data collectionsystem buffers an entire 360 degree scan of radar data. In otherembodiments, a fixed number of PRI, say 200, can be collected to formsectors, S_(k), and thereby minimize the memory requirements. Adetermination that all sectors have been processed can be based on asingle scan of 24 sectors, for example, or after multiple scans.

In one embodiment, the sea state is determined from a table containingvarious sea states that correspond to the ellipse parameters. Forexample, the eccentricity of an ellipse may correspond to a particularsea state. Sea state determination from the ellipse is described morefully below.

As is known in the art, radar signals from the sea surface are veryerratic, as can be readily seen in FIG. 8. The horizontal axis shows therange (in range bins of about 7.5 meters) and the vertical axis showsthe signal amplitude after passing through a Log-Amp detector and an8-bit Analog-to-Digital Converter (ADC).

As described in the '082 patent, a sea clutter range profile model thatis optimally smooth at the point where clutter descends below receivernoise, can have the form of a cubic:

${{C(r)} = {{Max}\left\{ {\mu,{\mu + {\frac{\xi - \mu}{h^{3}}\left( {h - r} \right)^{3}}}} \right\}}},$

where μ≈8 in FIG. 9 is the a priori known (calibrated) mean receivernoise level, ξ≈255 is the a priori known (calibrated) peak sea cluttersignal return that depends on parameters, such as pulse mode and antennaheight, and h is the environmentally influenced maximum range of seaclutter. The maximum sea clutter range h is written as h(α), where α isthe azimuth angle or aspect angle of the radar into the sea.

It should be noted that FIG. 9 shows the results of averaging about 200PRI of sea clutter video and fitting the result to the sea clutter modelabove. The mean receiver noise level μ, the peak sea clutter signalreturn ξ, and the maximum sea clutter range h(α) are shown.

Further data was collected using a navigation radar having videorecording with unprocessed radar video with a 12-bit ADC and returnsstored from a full 360 scan of the antenna (e.g., thousands of PRI)instead of being limited to sectors of about 10-15 degrees (about 200PRI).

FIG. 10 shows collected video data from a land-based system. Incomparison to the sea clutter in FIG. 8, several PRI of land clutter isshown in FIG. 10.

The collected data is stored as files referred to as “stochasticmatrices”, M, where M has the form M=M(α_(i), r_(j))={(α_(i), r₁, r₂, .. . , r_(j), . . . ): i=0, . . . , number of Azimuths per Revolution andj=1, . . . , number of range bins per PRI}. FIG. 11 shows an exemplaryrepresentation of the raw data. Each azimuth, α_(i), is a 12-bit binarynumber with 4095 being just a little smaller than 360°. Each range bin,r_(j), is the 8 MSBs of a 12-bit video sample at 20 MHz. Hence, eachrange bin represents the signal strength at a range increment of about7.5 meters. Recording of range bins was terminated at 6000 to savestorage space. 6000*7.5/1852=24.3 nmi, which is well beyond the Earth'shorizon for shipboard installations of typical navigation radars, forexample.

By putting the data, M, through a scan conversion process, the image inFIG. 11 can be viewed in a more traditional form shown in FIG. 12.

For processing convenience, the data M can be reorganized from the wayit is recorded into files, such as a list of the azimuth values,A={α_(i): i=0, . . . , number of Azimuths per Revolution}, and astochastic matrix, V={V(i, j): where i denotes the azimuth and j denotesthe range index}.

Somewhat arbitrarily, detection of sea clutter horizon was processed insectors of 15 degrees, thus, giving 24 sea clutter horizon estimatesaround the radar during each scan of the antenna. As a function of PRF,the radar gets a different number of PRI in each 15 degree sector. Thenumber of PRI per sector impacts performance as shown in Table 1 below.

TABLE 1 Typical Number of PRI per 15 Degree Sector PRFs (S-Band orX-Band) PRF (Hz) 2200 1800 1200 1028 600 # PRI per 229 187 125 107 62Sector The entries in the table should not be viewed as exact, butrather as nominal, since the PRF is jittered, the timing is approximate.The number of PRI per section can be computed as follows: # PRI perSector = (Scan Time/Sectors per Scan)/PRI = (2.5/24)*PRF = 0.104166*PRF.

Note that the values in Table 1 are based on a nominal antenna rotationspeed of 24 RPM, and thus, a sector scan time of 104.166 ms. By virtueof the antenna scanning and the elapsed time of 104.166 ms, the rangebins at a fixed range within a sector are not totally correlated. Withina shorter time, the sea clutter would be tightly correlated in range andazimuth by the smoothness of the ocean, the receiver bandwidth filter,and the beamwidth of the antenna.

The sampled radar video, V, can be described as a collection of sectors,S_(k), where each sector contains 6000 range bins from approximately 200PRI. Each sector has an associated azimuth, α_(k), which is the centerazimuth of all the PRI with range bins in S_(k). That is, the data isorganized into 24 sectors containing roughly 200×6000 range bins.

Whether an entire scan is collected in a single buffer, or if the datais collected in a smaller buffer of 200 PRIs, one can process sectors ofPRI associated with their middle azimuth, α_(k): S_(k)={_(k)(i, j):where i=1, . . . , N_(k) where N_(k) is the number of azimuths in thesector, and j=1, . . . , 6000 denotes the range index}. For each sector,the clutter horizon, h(α_(k)) is determined. The collection of{h(α_(k)): k=1, . . . , 24} for at least one full scan forms a data setH to which an ellipse is fitted.

One approach to finding h(α_(k)) for a sector, S_(k), is to form a rangeprofile by averaging across the N_(k)≈200 PRI forming the sector:

${{P_{k}(j)} = {\frac{1}{N_{k}}{\sum\limits_{i = 1}^{N_{k}}{V_{k}\left( {i,j} \right)}}}},$

where V_(k)(i, j) is the j^(th) range bin on the i^(th) PRI of sector k.The mean noise parameter of the sea clutter model described above iscalibrated and known a priori. The peak clutter return is possible tocalibrate, but is also found by looking for the near-range maximum valueof {P_(k)(j): j=1, . . . , 100}. A disadvantage in using the maximumversus a calibrated value is that sometimes the bow of ownship or asmall boat nearby can skew the parameter.

The only remaining parameter to find is h=h(α_(k)). This can be found,for example, using a standard best least mean square algorithm. LeastMean Square techniques are well known to one of ordinary skill in theart. One feature of least squares fitting is that it provides a naturalmetric by which to decide if the data fits well to the model or if itfits poorly. For example, given the average range profile, P_(k)(j), forthe sector and the best least mean square fit cubic polynomial, C(j)with μ and ξ calibrated and h determined by the fitting process, thealert is issued when the residual error is too large:

${{Alert}\mspace{14mu} {Issued}\mspace{14mu} {if}\mspace{14mu} {Residual}\mspace{14mu} {Error}} = {{\sum\limits_{j = 1}^{6000}{{{P_{k}(j)} - {C(j)}}}^{2}} > {{Pre}\text{-}{selected}\mspace{14mu} {Threshold}\mspace{14mu} {Value}}}$

It is understood that the threshold value can be selected to meet theneeds of a particular application.

Reasons for a poor fit, and generation of an alert, to the average rangeprofile can include: radar blanking sector, own ship wake, multi-pathinterference from own ship structures, targets, weather, such as rainclutter, interference, jamming, land, etc.

Since higher sea states are common with stormy weather, there may besensitivity to rain clutter. While sea state processing may be robustwith respect to isotropic rain when the precipitation surrounds theantenna for miles around, it is understood that processing may bedegraded when the precipitation is an isolated squall or an approachingweather front.

In one embodiment, the sea clutter horizon h(α_(k)) is determined usingMeans of Ratios of Order Statistics. While particular ratios are used inan exemplary embodiment, it is understood that a range of ratios can beused. Rather than forming P_(k)(j), we can form another range profile,Φ, through ratios of Order Statistics. We set

${\Phi_{k}(j)} = {\frac{{RV}\left( {{50\%},j} \right)}{{RV}\left( {{90\%},j} \right)}.}$

To compute RV(50%, j), we take the roughly 200 range bins at range binindex j, and sort them from lowest to highest. The median value atrange, j, will be the middle, RV(50%, j). The roughly 180^(th) largestvalue, the 90^(th) percentile, is RV(90%, j). A full sort of 200 samplesover 6000 range bins indices can be efficiently processed using wellknown sorting algorithms to generate a first trace Φ(j), in FIG. 13. Asecond trace in the figure is the mean clutter P(j)/ξ. A third trace TTis a low pass filter trace. Note that the way we have defined Φ, it isknown that 0≦Φ≦1.

It is believed that the small fluctuations in trace Φ(j) at close rangeare due to various factors, such as the smooth, correlated waves, andthe radar receiver log-amp compression of strong signals. Note that evenwhen the mean clutter P(j)/ξ has dropped to half of its strength, Φ ismaintaining a value near 0.8. Thus, as the mean is shrinking, either themedian is growing (skewness) or the standard deviation is shrinking inperfect compensation.

At ranges beyond sea clutter, it is expected that the radar videocontains mostly samples of receiver noise. Assuming that the receiverdetector is of a square law type, the noise sample amplitudes follow anexponential law with mean parameter, μ. The median value of anexponential distribution is found by solving

0.5=F(m)=1−e ^(−m/μ).

Hence, m=−μ·ln(0.50). With μ=8, we get RV(50%, large rangeindices)=5.55. The 90^(th) percentile is RV(90%, large rangeindices)=−μ·ln(0.1)=18.42. The ratio, the expected value of Φ over suchnoise, is E[Φ]=ln(0.5)/ln(0.1)=0.3. As can be seen in FIG. 13, this isthe value around which the Φ(j) trace fluctuates past range bin index450.

After sorting the sector S_(k) and finding the range profile, Φ_(k), asa ratio of order statistics (rank values), processing is performed tofind the best fit to a “Z-curve” selected for a model for Φ. Using knowntechniques, one can determine the coordinates of the ‘corner point’ CPin the Z-curve, as shown in the plots Φ_(k) of separate data sets andwaveforms in FIGS. 14A and 14B. The range, which corresponds to therange bin index, of this corner point CP is the estimate of the seaclutter horizon h(α_(k)). Thus, an estimate of maximum sea clutter rangeh(α_(k)) is found as the corner point in a Z-curve fit to Φ_(k). It isunderstood that the above corresponds to step 704 in FIG. 7.

The below corresponds to step 714 of FIG. 7, i.e., fitting an ellipse toa full set of horizon samples H. In one embodiment, the system can mapratios of ellipse parameters to the sea state, thus making itinsensitive to various calibrations that can be effected by magnetrondecay, VCO (voltage controlled oscillator) tuning, gain and bias tuning,and the like.

There are at least two equivalent ways to describe an ellipse. In oneembodiment, the more geometric approach using rotations and translations(congruences) of a standard equation is preferred as being moreintuitive. However, the general quadratic relation for an ellipse may bemore convenient for fitting to data. It is readily apparent that one cango back and forth between the two representations.

The equation of a standard ellipse in Cartesian coordinates (u, v) hasthe form:

${{\frac{u^{2}}{a^{2}} + \frac{v^{2}}{b^{2}}} = 1},$

where, if a>b, the parameter a is called the semi-major axis and b iscalled the semi-minor axis. This standard ellipse is centered at (0, 0)and has its axes on the coordinate axes. Every other ellipse in theCartesian plane is the result of a rotation or translation of an ellipsein standard form. So, a general ellipse in the Cartesian (x, y) planehas the form:

$\begin{pmatrix}x \\y\end{pmatrix} = {{\begin{pmatrix}{\cos (\theta)} & {- {\sin (\theta)}} \\{\sin (\theta)} & {\cos (\theta)}\end{pmatrix}\begin{pmatrix}u \\v\end{pmatrix}} + \begin{pmatrix}c \\d\end{pmatrix}}$

This form of the ellipse identifies 5 distinguishing parameters: thesemi-major and semi-minor axes, the rotation angle θ, and thetranslation offsets c and d. Additional parameters are functions of the5 basic ones. In particular, we highlight the eccentricity defined fora>b as

$e = {\sqrt{1 - \left( \frac{b}{a} \right)^{2}}.}$

The eccentricity e, a unitless function of the ratio b/a, enablesmapping an ellipse to sea state.

A general algebraic approach to ellipses expresses the ellipse as acertain quadratic relation:

c ₁ x ² +c ₂ xy+c ₃ y ² +c ₄ x+c ₅ y+c ₆=0.

To find the coefficients c_(n), we can solve the prior vector equationfor (x, y) and plug into the standard equation:

$\begin{matrix}{\begin{pmatrix}u \\v\end{pmatrix} = {\begin{pmatrix}{\cos (\theta)} & {\sin (\theta)} \\{- {\sin (\theta)}} & {\cos (\theta)}\end{pmatrix}\left( {\begin{pmatrix}x \\y\end{pmatrix} - \begin{pmatrix}c \\d\end{pmatrix}} \right)}} \\{{= \begin{pmatrix}{{{\cos (\theta)}\left( {x - c} \right)} + {{\sin (\theta)}\left( {y - d} \right)}} \\{{{\cos (\theta)}\left( {y - d} \right)} - {{\sin (\theta)}\left( {x - c} \right)}}\end{pmatrix}},}\end{matrix}$

so

${\frac{\left( {{{\cos (\theta)}\left( {x - c} \right)} + {\sin (\theta)\left( {y - d} \right)}} \right)^{2}}{a^{2}} + \frac{\left( {{{\sin (\theta)}\left( {x - c} \right)} - {{\cos (\theta)}\left( {y - d} \right)}} \right)^{2}}{b^{2}}} = 1$

and we get the relationships:

$c_{1} = {\frac{\cos^{2}(\theta)}{a^{2}} + \frac{\sin^{2}(\theta)}{b^{2}}}$$c_{2} = {\frac{2{\cos (\theta)}{\sin (\theta)}}{a^{2}} - \frac{2{\cos (\theta)}{\sin (\theta)}}{b^{2}}}$$c_{3} = {\frac{\sin^{2}(\theta)}{a^{2}} + \frac{\cos^{2}(\theta)}{b^{2}}}$$c_{4} = {- \begin{pmatrix}{\frac{{2{c \cdot {\cos^{2}(\theta)}}} + {2d\; \cos (\theta){\sin (\theta)}}}{a^{2}} +} \\\frac{{2c\; {\sin^{2}(\theta)}} - {2d\; {\cos (\theta)}{\sin (\theta)}}}{b^{2}}\end{pmatrix}}$ $c_{5} = {- \begin{pmatrix}{\frac{{2d\; {\sin^{2}(\theta)}} + {2c\; {\cos (\theta)}{\sin (\theta)}}}{a^{2}} +} \\\frac{{2d\; {\cos^{2}(\theta)}} - {2{c \cdot {\cos (\theta)}}{\sin (\theta)}}}{b^{2}}\end{pmatrix}}$ $c_{6} = {{- \begin{pmatrix}{\frac{{c^{2}{\cos^{2}(\theta)}} + {2c\; d\; {\cos (\theta)}{\sin (\theta)}} + {d^{2}{\sin^{2}(\theta)}}}{a^{2}} +} \\\frac{{d^{2}\; {\cos^{2}(\theta)}} - {2c\; d\; {\cos (\theta)}{\sin (\theta)}} + {c^{2}{\sin^{2}(\theta)}}}{b^{2}}\end{pmatrix}} - 1}$

Notice that a general quadratic relation of the form given above is anellipse only if the “discriminant” c₂ ²−4c₁c₃<0. This can be verified asfollows:

$c_{2}^{2} = {\frac{4{\cos^{2}(\theta)}{\sin^{2}(\theta)}}{a^{4}} + \frac{4{\cos^{2}(\theta)}{\sin^{2}(\theta)}}{b^{4}} - \frac{8{\cos^{2}(\theta)}{\sin^{2}(\theta)}}{a^{2}b^{2}}}$${{4c_{1}c_{2}} = {\frac{4{\cos^{2}(\theta)}{\sin^{2}(\theta)}}{a^{4}} + \frac{4{\cos^{2}(\theta)}{\sin^{2}(\theta)}}{b^{4}} + \frac{{4{\cos^{4}(\theta)}} + {4{\sin^{4}(\theta)}}}{a^{2}b^{2}}}},$

so

$\begin{matrix}{{c_{2}^{2} - {4c_{1}c_{3}}} = {- \frac{{8{\cos^{2}(\theta)}{\sin^{2}(\theta)}} + {4{\cos^{4}(\theta)}} + {4{\sin^{4}(\theta)}}}{a^{2}b^{2}}}} \\{= {- \frac{4\left( {{\cos^{2}(\theta)} + {\sin^{2}(\theta)}} \right)^{2}}{a^{2}b^{2}}}} \\{= {{- \frac{4}{a^{2}b^{2}}} < 0.}}\end{matrix}$

While the algebraic formulation of the ellipse supports ways of findingthe best fit to data, one can see the challenge of deriving from themthe more geometrically motivated and descriptive parameters. Theconversions are set forth below:

$c = {\frac{\left( {c_{3}{c_{4}/2}} \right) - \left( {c_{2}{c_{5}/4}} \right)}{{c_{2}^{2}/4} - {c_{1}c_{3}}} = \frac{{2c_{3}c_{4}} - {c_{2}c_{5}}}{c_{2}^{2} - {4c_{1}c_{3}}}}$$d = {\frac{\left( {c_{1}{c_{5}/2}} \right) - \left( {c_{2}{c_{4}/4}} \right)}{{c_{2}^{2}/4} - {c_{1}c_{3}}} = \frac{{2c_{1}c_{5}} - {c_{2}c_{4}}}{c_{2}^{2} - {4c_{1}c_{3}}}}$$a = \sqrt{\frac{2\left( {{c_{1}{c_{5}^{2}/4}} + {c_{3}{c_{4}^{2}/4}} + {c_{6}{c_{2}^{2}/4}} - {c_{2}c_{4}{c_{5}/4}} - {c_{1}c_{3}c_{6}}} \right)}{\left( {{c_{2}^{2}/4} - {c_{1}c_{3}}} \right)\left\lbrack {\sqrt{\left( {c_{1} - c_{3}} \right)^{2} + c_{2}^{2}} - \left( {c_{1} + c_{3}} \right)} \right\rbrack}}$$b = \sqrt{\frac{2\left( {{c_{1}{c_{5}^{2}/4}} + {c_{3}{c_{4}^{2}/4}} + {c_{6}{c_{2}^{2}/4}} - {c_{2}c_{4}{c_{5}/4}} - {c_{1}c_{3}c_{6}}} \right)}{\left( {{c_{2}^{2}/4} - {c_{1}c_{3}}} \right)\left\lbrack {{- \sqrt{\left( {c_{1} - c_{3}} \right)^{2} + c_{2}^{2}}} - \left( {c_{1} + c_{3}} \right)} \right\rbrack}}$$\theta = \left\{ \begin{matrix}0 & {{{if}\mspace{14mu} b} = {{0\mspace{14mu} {and}\mspace{14mu} c_{1}} > c_{3}}} \\{\pi/2} & {{{if}\mspace{14mu} b} = {{0\mspace{14mu} {and}\mspace{14mu} c_{3}} > c_{1}}} \\{{\frac{1}{2}{Arc}\; {{Tan}\left( {{c_{1} - c_{3}},c_{2}} \right)}}\mspace{14mu}} & \begin{matrix}{{being}\mspace{14mu} {sure}\mspace{14mu} {to}\mspace{14mu} {properly}} \\{\; {{wrap}\mspace{14mu} {the}\mspace{14mu} {angle}}}\end{matrix}\end{matrix} \right.$

We expect to have the radar measure the clutter horizon at pointssampled uniformly in azimuth. So, we expect a set of points P_(i) inpolar coordinates H={(α_(i), (α_(i))): α_(i)=2π(i−1)/N for i=1, 2, . .. , N}, or in Cartesian coordinates H′={((α_(i)) cos(α_(i)), (α_(i))sin(a_(i))) i=1, 2, . . . , N}, where N is a reasonably small number,e.g., N=24. If H describes an ellipse around the radar, then a goodestimator of the center of the ellipse is the average of the samplepoints in H′:

${offset} = {\begin{pmatrix}{c,} & d\end{pmatrix}^{T} = {\begin{pmatrix}c \\d\end{pmatrix} \approx {\begin{pmatrix}{\frac{1}{N}{\sum\limits_{i = 1}^{N}{{h\left( \alpha_{i} \right)}{\cos \left( \alpha_{i} \right)}}}} \\{\frac{1}{N}{\sum\limits_{i = 1}^{N}{{h\left( \alpha_{i} \right)}{\sin \left( \alpha_{i} \right)}}}}\end{pmatrix}.}}}$

If the sea is well-developed, i.e., the wind is not shifting, there isno reason to expect the clutter horizon to be asymmetrical from theradar's point of view. In other words, the offset vector (c, d)^(T)should be parallel to the major axis of the ellipse. If the offset isnot parallel, an alert can be issued, as in step 718 of FIG. 7.

If (c, d)^(T) is reasonably parallel to the ellipse's semi-major axis,we set the estimate for the sea direction to be:

θ=If {d>0, ArcTan(c,d), else 2π+ArcTan(c,d)}.

It is understood that sea state direction refers to the direction of theswells generated by wind.

After centering the measured points, the semi-major axis, a, and thesemi-minor axis, b, may be found as the maximum and minimum:

a≈max{∥P _(i)−offset∥,i=1, 2, . . . , N}=max{√{square root over((h(α_(i))cos(α_(i))−c)²+(h(α_(i))sin(α_(i))−d)²)}{square root over((h(α_(i))cos(α_(i))−c)²+(h(α_(i))sin(α_(i))−d)²)}{square root over((h(α_(i))cos(α_(i))−c)²+(h(α_(i))sin(α_(i))−d)²)}{square root over((h(α_(i))cos(α_(i))−c)²+(h(α_(i))sin(α_(i))−d)²)}}

b≈min{∥P _(i)−offset∥,i=1, 2, . . . , N}=min{√{square root over((h(α_(i))cos(α_(i))−c)²+(h(α_(i))sin(α_(i))−d)²)}{square root over((h(α_(i))cos(α_(i))−c)²+(h(α_(i))sin(α_(i))−d)²)}{square root over((h(α_(i))cos(α_(i))−c)²+(h(α_(i))sin(α_(i))−d)²)}{square root over((h(α_(i))cos(α_(i))−c)²+(h(α_(i))sin(α_(i))−d)²)}}

Given a and b, we have the eccentricity of the sea clutter, which can bemapped to the sea state, the significant wave height, and/or the RMSwave height.

At this point, we have estimators for all 5 parameters of an ellipse: a,b, c, d, and θ. The equation for the corresponding ellipse can bederived from them:

${{\frac{x^{2}}{a^{2}} + \frac{y^{2}}{b^{2}}} = 1},$

and

$\begin{pmatrix}p_{1} \\p_{2}\end{pmatrix} = {{\begin{pmatrix}{\cos (\theta)} & {- {\sin (\theta)}} \\{\sin (\theta)} & {\cos (\theta)}\end{pmatrix}\begin{pmatrix}x \\y\end{pmatrix}} + {\begin{pmatrix}c \\d\end{pmatrix}.}}$

We can measure how closely the points in data set H′ match the ellipsedescribed above, and when the match is less than a threshold, asdescribed below, we can generate an exception and issue an alert.

In one embodiment, the error function is the sum of all of thedifferences between the model points (p₁(α_(k)), p₂(α_(k)))^(T) and theclutter horizon measurements, h(α_(k)). An alert can be generated if theresidual is great than a pre-selected value:

${{Alert}\mspace{14mu} {Issued}\mspace{14mu} {if}\mspace{14mu} {Residual}\mspace{14mu} {Error}} = {{\sum\limits_{k = 1}^{N}{{\sqrt{{P_{1}^{2}\left( \alpha_{k} \right)} + {P_{2}^{2}\left( \alpha_{k} \right)}} - {h\left( \alpha_{k} \right)}}}^{2}} > {{Pre}\text{-}{selected}\mspace{14mu} {Threshold}\mspace{14mu} {Value}}}$

In one embodiment, the clutter is sampled repeatedly for averaging theestimates of eccentricity over time to reduce randomness. Since the seastate varies slowly, this should provide reliable results. Each set, H,is an over-sampling of the ellipse, so a least mean squares fit of ageneral ellipse to the points in H should provide significant noisereduction on each measurement of eccentricity.

Once fitting to an ellipse is completed, the ellipse parameters can bemapped to sea state and direction, as in step 722 of FIG. 7. Given the 5ellipse parameters, {a, b, θ, c, d}, when θ is close to the direction ofthe offset vector (c, d)^(T), either of these angles will provide a goodestimate of the direction of the waves. When these angles are notreasonably close, the system considers the sea state to be immature andasserts an exception or alerts the operator. For example, FIG. 6 shows aPPI with the radar position offset from the clutter ellipse center. Thewind direction appears to be from near True Azimuth of 300° or about 5o'clock relative to the figure. The direction of the radar positionoffset is roughly parallel to the long axis of the clutter ellipse.

The required closeness of θ and the offset vector (c, d)^(T) can beselected to meet the needs of a particular application. In oneembodiment, the system requires θ and the offset vector (c, d)^(T) to bewithin ten degrees.

A variety of ellipse parameters may be useful in determining the seastate. For example, one parameter is the eccentricity of the ellipsefitting the sea clutter horizon. Another parameter is the ratio of thelength of the offset vector to the semi-major axis. It is understoodthat the length of the offset vector and the semi-major axis areexpected to grow in higher sea states.

In symbols, we expect to find a value of ε>0 so that

${{Sea}\mspace{14mu} {Direction}} = \frac{\theta + {{Arc}\; {{Tan}\left( {{- c},{- d}} \right)}}}{2}$

when |θ−ArcTan(−c,−d)|<ε,Table 2 below shows an exemplary mapping to sea state.

TABLE 2 WMO Sea State Ellipse Eccentricity (c² + d²)/a² 0 <0.200 <0.05 1<0.350 <0.1 2 <0.400 <0.15 3 <0.450 <0.175 4 <0.500 <0.2 5 <0.525 <0.2256 <0.540 <0.25Table 3 below shows further exemplary mapping information.

WMO Sea State e(c² + d²)/a² 0 <0.01 1 <0.035 2 <0.060 3 <0.079 4 <0.1005 <0.115 6 <0.130

As the radar continuously collects sea clutter data and determines newvalues for the descriptors in the mapping tables, such as every fewseconds, a low pass filter (step 724 in FIG. 7) can be applied to thedata to prevent sea state values from jumping erratically. In oneembodiment, an infinite impulse response (IIR) filter with a 10 minutetime constant is used. It is understood that any suitable filter can beused to meet the needs of a particular application.

For example, set z_(n)=estimate of e(c²+d²)/a² on the n^(th) scan of theradar after a mode change. Assume each radar scan takes 2.5 seconds.After 10 seconds, set y₄=(z₁+z₂+z₃+z₄)/4. Use y₄ as the value ofe(c²+d²)/a² to determine the sea state via the table. On subsequentscans, n>4, use the equation

y _(n) =y _(n-1)+α(z _(n) −y _(n-1)).

If y₄=0 and z_(n)=1 for all n>4, determine how long it would take fory_(n) to be as large as 1−1/e where this “e” is Euler's constant.Looking at the geometric progression y₅=α, y₆=α+α(1−α),y₇=α+α(1−α)+α(1−(α+α(1−α)))=α(1+(1−α)+(1−α)²),y₈=α(1+(1−α)+(1−α)²+(1−α)³), . . . , by induction,

$y_{n + 4} = {{\alpha {\sum\limits_{i = 0}^{n - 1}\left( {1 - \alpha} \right)^{i}}} = {1 - {\left( {1 - \alpha} \right)^{n}.}}}$

Solve for α by setting 1−(1−α)^(n+1)=1−1/e. Then, (1−α)^(n+1)=1/e, and(n+1)ln(1−α)=−1. Hence, α=1−e^(−1/(n+1)). For a 10 minute time constantat 2.5 second updates, we get n=10*60/2.5=240, and so α=0.004.

Similar filtering can also be applied to the estimates of the sea statedirection.

It is understood that a variety of techniques can be used to fit thecollected data to an ellipse. For example, a least squares curve fittingtechnique can be used to minimize the sum of the squares of theresiduals. By minimizing the residuals, the ellipse that most closelyfits to the data can be identified.

Ellipse fitting can also be performed using a Fourier Transform, such asa digital Fourier Transform (DFT). One can describe the general ellipsein a Cartesian plane with 5 parameters: a and b along with a rotationangle, θ, and translation distances along the coordinate axes, c and d.Set x=a cos(α) and y=b sin(α) and see that any point (x, y)^(T) on thestandard ellipse is transformed to a point, P, on the general ellipse

$\begin{matrix}{P = \begin{pmatrix}p_{1} \\p_{2}\end{pmatrix}} \\{= {{R_{\theta}\begin{pmatrix}{a \cdot {\cos (\alpha)}} \\{b \cdot {\sin (\alpha)}}\end{pmatrix}} + \begin{pmatrix}c \\d\end{pmatrix}}} \\{{= \begin{pmatrix}{{{a \cdot {\cos (\alpha)}}{\cos (\theta)}} - {{b \cdot {\sin (\alpha)}}{\sin (\theta)}} + c} \\{{{a \cdot {\cos (\alpha)}}{\sin (\theta)}} + {{b \cdot {\sin (\alpha)}}{\cos (\theta)}} + d}\end{pmatrix}},}\end{matrix}$

where R_(θ) is a rotation matrix by the angle θ. Our “clutter horizon”,h, is modeled here as the length of a vector (range) from the origin toP:

$h = {\sqrt{\begin{matrix}{\left( {{{a \cdot {\cos (\alpha)}}{\cos (\theta)}} - {{b \cdot \sin}(\alpha){\sin (\theta)}} + c} \right)^{2} +} \\\left( {{{a \cdot {\cos (\alpha)}}{\sin (\theta)}} + {{b \cdot {\sin (\alpha)}}{\cos (\theta)}} + d} \right)^{2}\end{matrix}}.}$

One estimator of the center of the ellipse is the average of the samplepoints in H′:

$\begin{pmatrix}c \\d\end{pmatrix} \approx {\begin{pmatrix}{\frac{1}{N}{\sum\limits_{i = 1}^{N}{{h\left( \alpha_{i} \right)}{\cos \left( \alpha_{i} \right)}}}} \\{\frac{1}{N}{\sum\limits_{i = 1}^{N}{{h\left( \alpha_{i} \right)}{\sin \left( \alpha_{i} \right)}}}}\end{pmatrix}.}$

Once the ellipse is “centered” by subtracting (c, d)^(T) from the data,the form of h becomes simpler:

${h\left( {{\alpha;a},b,0,0,\theta} \right)} = {\sqrt{\begin{matrix}{\left( {{{a \cdot {\cos (\alpha)}}{\cos (\theta)}} - {{b \cdot \sin}(\alpha){\sin (\theta)}}} \right)^{2} +} \\\left( {{{a \cdot {\cos (\alpha)}}{\sin (\theta)}} + {{b \cdot {\sin (\alpha)}}{\cos (\theta)}}} \right)^{2}\end{matrix}}.}$

This simpler form of h (with exaggerated eccentricity) is shown in FIG.15. Characteristic of the function h for a centered ellipse is that itcompletes two periods as the polar angle sweeps out 360 degrees. Themaxima and minima of this centered h are the semi-major and semi-minoraxes of the ellipse.

The DFT applied to samples {h(α₁), . . . , h(α_(N))} provides thecoefficient of simpler function, f, that we chose for:

f(α;c ₁ ,c ₂ ,c ₃)=c ₁ +c ₂ Cos(2α)+c ₃ Sin(2α),

as shown in FIG. 15.Here, c₁ is the average value of h,

$c_{1} \approx {\frac{1}{N}{\sum\limits_{i = 1}^{N}{h\left( \alpha_{i} \right)}}}$

The other coefficients c₁ and c₂ are the real and imaginary parts of theoutput in the second DFT filter. Recall that the “h” above is the “h”after centering.

Note that the focus is not h, but rather, the ellipse parameters. Thesemi-major axis, a, is the maximum of the centered h and the semi-minoraxis, b, is the minimum value. So to find a and b, we want to take thederivative of f with respect to α and set it equal to zero:

0=−2c ₂ Sin(2α)+2c ₃ Cos(2α),

so the values of interest are:

Tan(2α)=c ₃ /c ₂, or α₁=ArcTan(c ₃ ,c ₂)/2 and α₂=α₁+π/2.

As shown in FIG. 16, one or more sectors of a scan can be compromised,such as by own ship wake, own ship structures, weather, nearby vessels,etc. The data for a compromised sector can be replaced so as to enable360 degree processing. In one embodiment, data from each side of thecompromised sector is averaged and used to replace the compromisedsector. It is understood that a variety of techniques can be used toaddress compromised sectors. For example, a constrained matrix can beused, as described in A. Fitzgibbon et al, “Direct Least Square FittingEllipses,” IEEE Trans. On Pattern Analysis and Machine Intelligence,”21(5), May 1999, 476-480.

FIG. 17 shows an exemplary computer implementation to provide at leastpart of sea clutter processing in accordance with exemplary embodimentsof the invention. A computer includes a processor 802, a volatile memory804, a non-volatile memory 806 (e.g., hard disk), a graphical userinterface (GUI) 808 (e.g., a mouse, a keyboard, a display, for example)and an output device 809. The non-volatile memory 806 stores computerinstructions 812, an operating system 816 and data 818, for example. Inone example, the computer instructions 812 are executed by the processor802 out of volatile memory 804 to perform all or part of the signalreturn processing.

Processing may be implemented in computer programs executed onprogrammable computers/machines that each includes a processor, astorage medium or other article of manufacture that is readable by theprocessor (including volatile and non-volatile memory and/or storageelements), at least one input device, and one or more output devices.Program code may be applied to data entered using an input device toperform processing to generate output information.

The system may be implemented, at least in part, via a computer programproduct 811, (e.g., in a machine-readable storage device), for executionby, or to control the operation of, data processing apparatus (e.g., aprogrammable processor, a computer, or multiple computers)). Each suchprogram may be implemented in a high level procedural or object-orientedprogramming language to communicate with a computer system. However, theprograms may be implemented in assembly or machine language. Thelanguage may be a compiled or an interpreted language and it may bedeployed in any form, including as a stand-alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program may be deployed to be executed on onecomputer or on multiple computers at one site or distributed acrossmultiple sites and interconnected by a communication network. A computerprogram may be stored on a storage medium or device (e.g., CD-ROM, harddisk, or magnetic diskette) that is readable by a general or specialpurpose programmable computer for configuring and operating the computerwhen the storage medium or device is read by the computer to performprocessing. Processing may also be implemented as a machine-readablestorage medium 811, configured with a computer program, where uponexecution, instructions in the computer program cause the computer tooperate. While exemplary embodiments of the invention are shown havingillustrative partitions between hardware and software, it is understoodthat other configurations and partitions will be readily apparent to oneof ordinary skill in the art.

Having described exemplary embodiments of the invention, it will nowbecome apparent to one of ordinary skill in the art that otherembodiments incorporating their concepts may also be used. Theembodiments contained herein should not be limited to disclosedembodiments but rather should be limited only by the spirit and scope ofthe appended claims. All publications and references cited herein areexpressly incorporated herein by reference in their entirety.

1. A method, comprising: receiving radar return information from signalstransmitted by a radar; processing the radar return information toidentify sea clutter; and processing, using a processor, the sea clutterto fit an ellipse to a range horizon of the sea clutter as a function ofazimuth to determine a sea state.
 2. The method according to claim 1,further including determining a direction for the sea state based uponat least one parameter of the ellipse.
 3. The method according to claim1, further including generating an alert when the range horizon of thesea clutter does not fit an ellipse with a predetermined criteria. 4.The method according to claim 1, further including generating an alertwhen the range horizon for the sea clutter cannot be determined.
 5. Themethod according to claim 1, further including using ratios of orderstatistics to form a range profile for the sea clutter.
 6. The methodaccording to claim 5, further including determining the range horizonfor the sea clutter from a corner point of the range profile from theratios of order statistics.
 7. The method according to claim 2, furtherincluding determining the direction for the sea state from a directionof the major axis of the ellipse.
 8. The method according to claim 1,further including determining the sea state from an eccentricity of theellipse.
 9. The method according to claim 1, further includingdetermining the sea state from a ratio of a length of an offset of theellipse to a semi-major axis of the ellipse.
 10. The method according toclaim 1, further including mapping at least one parameter of the ellipseto sea states.
 11. The method according to claim 1, further includingusing a Fourier Transform to determine one or more parameters of theellipse.
 12. An article, comprising: non-transitory instructions storedon a computer readable medium to enable a machine to perform: receivingradar return information from signals transmitted by a radar; processingthe radar return information to identify sea clutter; and processing thesea clutter to fit an ellipse to a range horizon of the sea clutter todetermine a sea state.
 13. The article according to claim 12, furtherincluding instructions for determining a direction for the sea statebased upon at least one parameter of the ellipse.
 14. The articleaccording to claim 12, further including instructions for using ratiosof order statistics to form a range profile for the sea clutter.
 15. Thearticle according to claim 12, further including instructions fordetermining the range horizon for the sea clutter from a corner point ofthe range profile from the ratios of order statistics.
 16. The articleaccording to claim 13, further including instructions for determiningthe direction for the sea state from a direction of the major axis ofthe ellipse.
 17. The article according to claim 1, further includinginstructions for determining the sea state from an eccentricity of theellipse.
 18. A radar system comprising: an antenna to receive radarreturn; and a processor to process the radar return to identify seaclutter and fit a range horizon of the sea clutter to an ellipse todetermine a sea state.
 19. The system according to claim 18, wherein theprocessor can determine a direction for the sea state based upon atleast one parameter of the ellipse.